Hyperspectral identification of rice diseases and pests based on principal component analysis and probabilistic neural network

Li Bo; Liu Zhanyu; Huang Jingfeng; Zhang Lili; Zhou Wan; Shi Jingjing

Volume 25 Issue 9

Sep. 2009

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Abstract

Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2009 > 25(9): 143-147.

Li Bo, Liu Zhanyu, Huang Jingfeng, Zhang Lili, Zhou Wan, Shi Jingjing. Hyperspectral identification of rice diseases and pests based on principal component analysis and probabilistic neural network[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2009, 25(9): 143-147.

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Hyperspectral identification of rice diseases and pests based on principal component analysis and probabilistic neural network

Li Bo^1,2,
Liu Zhanyu^2,3,4,
Huang Jingfeng^2,3,4,
Zhang Lili⁵,
Zhou Wan⁵,
Shi Jingjing⁴

1.
Institute of Public Administration, Zhejiang University, Hangzhou 310028, China
2.
Key Laboratory of Agricultural Remote Sensing and Information System in Zhejiang Province, Hangzhou 310029, China
3.
Research Center of Agricultural Information Science and Technology, Zhejiang University in Zhejiang Province, Hangzhou 310029, China
4.
Institute of Agricultural Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310029, China
5.
General Station of Plant Protection and Soil-fertilizer of Hangzhou, Hangzhou 310020, China

More Information

Received Date: August 24, 2008
Revised Date: November 30, 2008
Published Date: September 29, 2009

Graphical Abstract

Abstract

Abstract

Correct and fast identification of rice diseases and pests was the basis of diseases and pests prevention measures, and significant in disaster assessment. This study adopted spectral reflectance of rice leaves stressed by rice Aphelenchoides besseyi Christie of two periods at the rice booting stage and by rice leaf roller of two periods at the rice tillering stage. With the analysis of the spectral characteristics of rice leaves, visible band (490-670 nm) and short wave infrared band (1 520-1 750 nm) were selected. The principal components spectrum were obtained with principal component analysis (PCA) transformed from the above two selected band. The recognition precision of rice Aphelenchoides besseyi Christie and rice leaf roller using probabilistic neural network (PNN) was as high as 95.65%. The research demonstrated that the method was feasible and reliable to precisely identify non-healthy rice stressed by rice diseases and pests from healthy rice based on PCA and PNN.
- spectrum analysis,
- principal component analysis,
- neural networks,
- rice

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